LAUSR

Coarse-grained reconfigurable architectures (CGRAs) are expected to be used for embedded systems, Internet of Things (IoT) devices, and edge computing thanks to their high-energy efficiency and programmability. In essence, a CGRA is an array of numerous processing elements. To exploit this abundant computation resource, a compiler for CGRAs has to fulfill more tasks compared that for general-purpose processors. Therefore, many studies have proposed optimization methods, especially for application mapping, because the performance and energy efficiency strongly depend on optimization at compile time. However, many works focus only on performance improvement or resource minimization, although such optimization objectives are not always appropriate when considering various use cases. In this work, we propose GenMap, an application mapping framework using multiobjective optimization based on a genetic algorithm so that users can set optimization criteria as needed. Besides, it provides aggressive power optimization using our dynamic power model and leakage minimization technique. The proposed method is applied to three fabricated CGRA chips for evaluation. Experimental results show that GenMap achieves 15.7% reduction of wire length while keeping processing element utilization when compared with conventional methods. In addition, according to real chip experiments, 12.1%–46.8% of energy consumption is reduced, and up to $2\times $ speedup is archived for several architectures when compared with other two approaches.